多任务多代理增强学习与交互和任务表示
IEEE transactions on neural networks and learning systems
|October 30, 2024
概括
代表交互和任务 (RIT) 通过描述代理互动和任务关系来增强多任务多代理强化学习. 这种新的算法改善了类似任务之间的知识传输,以提高性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 多代理系统 多代理系统
背景情况:
- 多任务多代理强化学习 (MT-MARL) 利用跨任务的知识来提高性能.
- 当前的方法学习了关于共享表示的独立政策,限制了明确的交互和任务关系分析.
研究的目的:
- 提出代表交互和任务 (RIT),一个新的MT-MARL算法.
- 为了有效的知识转移,明确描述任务内部代理互动和任务之间的任务关系.
主要方法:
- RIT使用交互式值分解来建模代理依赖性,接近交互表示的Shapley值.
- 从代理轨迹中学习任务表示,以识别任务相似性和关系.
- 一个通用的政策结构支持可扩展的多任务学习.
主要成果:
- 在类似的多代理任务中,RIT有效地转移交互知识.
- 评估显示,与最先进的基线相比,业绩有了显著的改善.
- 该算法在多任务和零射击学习设置中都表现出有效性.
结论:
- 理工学院通过明确建模代理互动和任务关系,为MT-MARL提供了一种新的方法.
- 该方法增强了合作多代理系统中的知识传输和可扩展性.
- 在复杂的MT-MARL场景中,RIT实现了卓越的性能,突出了其在复杂MT-MARL场景中的有效性.
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